AI Consultancy.
AI consultancy is the work you do before you spend money building anything: establishing which parts of your business AI would genuinely improve, what that improvement is worth against the P&L, what it would cost to run and maintain, and which options you should reject. Molo is an independent AI consultancy in London working with founders, CTOs, operations directors and marketing leaders on process audits, opportunity sizing, build-versus-buy decisions, data readiness and proportionate governance. We take no resale commissions, which is the only reason we can afford to end an engagement by recommending you buy nothing.
- Discipline
- AI Consultancy
- Studio
- London
- Engagements
- Project · Retainer
- Brands served
- 200+
How we work
AI strategy evaluated against your P&L, not the hype cycle.
A good AI consultancy engagement produces a decision, not a deck. That means a shortlist of initiatives with expected value and cost attached, a recommended sequence with a named owner for each, the reasoning behind every build-or-buy call, an honest read on whether your data supports any of it, and an explicit list of what you should not attempt this year. If an engagement ends with enthusiasm rather than a defensible number, it has failed, however good the slides were.
Evaluating an AI opportunity against the P&L is arithmetic rather than vision. We cost the process as it runs today at fully loaded rates: the hours, the people, the rework when it goes wrong, the delay cost while cases sit in a queue. Against that we set what proportion of cases a system could realistically handle end to end, what happens to the remainder, and the running cost of the system itself: model usage, integration upkeep, monitoring, and the human time spent supervising it. Most initiatives that fail this test fail on run cost, or on a containment rate that was assumed rather than tested. We test it against your real historical cases before anyone commits a build budget.
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Build versus buy usually has a defensible answer, and it is more often buy than agencies care to admit. Buy when the problem is common, the data involved is not your differentiator, and a vendor's roadmap points where you are already going: transcription, meeting notes, document extraction and support deflection generally sit here. Build when the process is the thing you compete on, when integration into your own systems is the bulk of the work anyway, or when vendor pricing scales with a volume metric that punishes your growth. A third answer is frequently the right one: buy the commodity layer and build only the thin part that is genuinely yours.
Data readiness is the most common blocker and the least discussed. Before recommending anything we test whether the information a system would need actually exists in retrievable form, whether it is accurate and current, who owns it, and whether it can lawfully be used for the purpose you have in mind. A striking number of AI projects turn out to be data projects wearing a costume: the model was never the constraint. Where that is the case we say so, and the roadmap opens with unglamorous work on structure and ownership that pays back across every later initiative rather than a pilot that was never going to survive contact with your records.
Governance means deciding in advance who is accountable for a system's output, what it may do without supervision, how its decisions are logged, how somebody affected by one can contest it, and what happens when it is wrong. We help you write that down at a proportionate level: a small company needs named ownership and a documented escalation route, not a policy framework designed for a bank. We treat obligations around personal data, customer disclosure and record-keeping as design inputs rather than a compliance afterthought, and where decisions are regulated we will tell you to take your own legal advice rather than pretend to provide it.
Some of the most valuable output of an engagement is the list of things not to do. We advise against AI where errors are unrecoverable or unbounded; where volume is too low to repay a build and its maintenance; where the process is undocumented because each case turns on tacit judgement nobody has written down; where configuring a system you already own would solve the problem; and where the honest driver is that a board asked what the AI strategy is. We also flag where automating a customer-facing interaction would strip out the human contact that is a reason people buy from you at all. Saying no early is the cheapest deliverable we produce.
Our independence is structural, not a claim. We take no resale commissions, referral fees or vendor incentives, and we hold no partnerships we would be tempted to defend. We will often build what we recommend through our AI agents and automation practice, but the consultancy is priced and delivered separately from any build commitment, and we hand over cleanly to an internal team or another partner when that is the better fit. We have not published a client AI case study and will not imply otherwise. Our evidence is the engineering and measurement discipline behind platform work such as the Circle Of Life subscription engine and the Glass Openings configurator, product strategy on the Lumina reporting platform, and growth engagements where results were visible in the client's own analytics. In a market full of unverifiable claims, that distinction is the point.
Capabilities
Everything you need under one roof.
Each engagement is shaped around the outcome you’re hiring us for. Below is the full toolkit our team brings to ai consultancy engagements.
- Process and opportunity audits
- Opportunity sizing against the P&L
- Containment and run-cost modelling
- Build versus buy assessment
- Data readiness and ownership review
- Vendor evaluation with no resale relationships
- Prioritised AI roadmap and sequencing
- Governance, accountability and escalation design
- Risk register and do-not-automate list
- Pilot design and success criteria
- Second opinion on an AI proposal you have received
- Team training and internal enablement
Our Process
A clear path from brief to launch.
We’ve refined this process across hundreds of engagements. It scales from a four-week sprint to a multi-year partnership without losing momentum.
Discovery
Stakeholder interviews across the teams doing the work and the ones paying for it, process mapping as things actually happen, and an audit of the tooling and data already in place. We are looking for where time and money leak, not for somewhere to put AI.
Baseline
We cost the candidate processes as they run today: volume, time per case, fully loaded staff cost, rework, delay cost and current error rate. Every recommendation that follows is measured against these figures, which makes optimistic business cases much harder to write.
Opportunity sizing
Each initiative is scored on value, implementation risk, time to impact and ongoing run cost. We test the containment assumption against your real historical cases rather than a vendor demo, because that single number decides whether most business cases hold together.
Build versus buy
For each surviving initiative we assess whether to buy, build or combine the two, reviewing vendors on fit, pricing model, data handling, exit cost and roadmap direction. We have no resale relationships, so the recommendation follows the evidence including where it points at doing nothing.
Roadmap and governance
A sequenced plan with owners, success metrics, budget and dependencies, paired with a proportionate governance model: accountability, permitted autonomy, logging, escalation and review. The explicit do-not-do list is delivered alongside it, with the reasoning attached.
Enablement
Optional ongoing advisory as initiatives ship, plus training so your team can evaluate future AI proposals without us. The measure of success is that you need less consultancy next year, not more.
Outcomes
Proof, in the only currency that matters.
Typical audit-to-roadmap timeline
Initiatives surfaced per audit
Vendor-independent recommendations
FAQ
Common questions.
Don’t see your question? Email info@molo.agency and we’ll come back within one working day.
Do I need an AI strategy or just a tool?
For many businesses, a tool. If you have one obvious repetitive process and a clear owner, buy something sensible, run it for a quarter and learn from it. You need a strategy when decisions start interacting: multiple teams want different tools, data would have to move between them, budget is being committed across a year, or a first attempt has already failed and nobody agrees on why.
What does an AI consultancy engagement produce?
A decision, documented. That means a shortlist of initiatives with expected value and cost attached, a recommended sequence with owners, the reasoning behind each build-or-buy call, a data readiness assessment, a proportionate governance model, and an explicit list of what not to do. If the honest conclusion is that nothing clears the bar this year, that is the deliverable and we will say it plainly.
How do you decide whether an AI project is worth doing?
Arithmetic. We cost the process today at fully loaded rates including rework and delay, estimate what share of cases a system could realistically handle end to end, and set that against build cost plus ongoing run cost: model usage, integration upkeep, monitoring and human supervision. We test the containment assumption against your real historical cases, because that is the number optimistic business cases most often get wrong.
Should we build our own AI or buy an off-the-shelf product?
Buy when the problem is common, the data is not your differentiator, and a vendor's roadmap points where you are going: transcription, document extraction, meeting notes and support deflection usually sit here. Build when the process is what you compete on, when integration into your own systems is most of the work anyway, or when vendor pricing scales punitively with your growth. Often the answer is buy the commodity layer and build the thin part that is yours.
Is our data ready for AI?
Often less than expected, and it is the most common reason projects stall. The test is whether the information a system would need exists in retrievable form, is accurate and current, has a clear owner, and can lawfully be used for the intended purpose. A large share of AI projects are data projects in disguise. Where that is true, the roadmap starts with structure and ownership work that benefits every later initiative.
Where should we not use AI?
Where errors are unrecoverable or unbounded. Where volume is too low to repay the build and its maintenance. Where the process is undocumented because each case turns on tacit judgement. Where configuring a system you already own would solve it. Where the driver is that a board asked about AI rather than a business problem. And where removing human contact would remove a reason people choose you.
What does AI governance actually involve?
Deciding in advance who is accountable for a system's output, what it may do without supervision, how its decisions are logged, how someone affected can contest one, and what happens when it is wrong. It should be proportionate: a small company needs named ownership and a documented escalation route, not a bank's policy framework. Where decisions are regulated, we will tell you to take your own legal advice rather than substitute for it.
How much does AI consultancy cost?
It scales with scope: how many processes are examined, how many stakeholders and systems are involved, and whether you want a one-off assessment or ongoing advisory as initiatives ship. A focused review of a single function is a different proposition to an organisation-wide audit. We would rather scope a narrow engagement that produces a decision than a long one that produces a document.
Are you tied to any AI vendors?
No. We take no resale commissions, referral fees or vendor incentives, and we hold no partnerships we would be tempted to defend. That independence is the only reason we can afford to recommend against a purchase, including against work we would otherwise be paid to build. Ask any consultancy how it is remunerated before you weigh its recommendations.
Will you build what you recommend?
Sometimes, through our AI agents and automation practice, but the consultancy is priced and delivered separately from any build commitment. If the right answer is an off-the-shelf product, your internal team, or a different partner, we say so and hand over cleanly with the assessment and requirements. A consultancy that only ever recommends its own build capability is running a sales process.
Decided what to build?
Once the business case holds, our AI agents and automation practice engineers it into the systems you already run: scoped tightly, supervised, and measured against the baseline we took.
Real businesses. Real results.
Every project we ship is measured against commercial outcomes, not vanity metrics. These are three of the brands we’ve helped scale this year.
Let’s discuss a project.
Tell us about your business, your ambition, and your timing. We’ll come back within one working day with the right shape of partnership for what you’re trying to build.
- info@molo.agency
- Phone
- +44 207 255 2221
- Studio
- 1st Floor Woodgate Studios, 2-8 Games Road
Cockfosters EN4 9HN
United Kingdom